ArticleJournal of the Royal Society, Interface2018
A mechanistic hydro-epidemiological model of liver fluke risk.
Article in Journal of the Royal Society, Interface, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers, 1 of them a synthesis that pooled it.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
10 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Environmental influences on the distribution and ecology of the fluke intermediate hostParasitology · 2024Pooled it
- Novel environmental risk index reveals uneven impact of climate change on fascioliasis risk across Africa and Europe.PLoS neglected tropical diseases · 2026Article
- Fasciola hepatica in alpine dairy farming: prevalence trends, risk factors and associations with Salmonella Dublin seropositivity.BMC veterinary research · 2026Article
- Ecological Niche Modelling Approaches: Challenges and Applications in Vector-Borne Diseases.Tropical medicine and infectious disease · 2023Review
- Understanding the role of wild ruminants in anthelmintic resistance in livestock.Biology letters · 2022Review
- Ecohydrology 2.0.Rendiconti Lincei. Scienze fisiche e naturali · 2022Review
- Discovering environmental management opportunities for infectious disease control.Scientific reports · 2021Article
- Rapid detection of Galba truncatula in water sources on pasture-land using loop-mediated isothermal amplification for control of trematode infections.Parasites & vectors · 2020Article
- What Modeling Parasites, Transmission, and Resistance Can Teach Us.The Veterinary clinics of North America. Food animal practice · 2020Review
- Comparative assessment of faecal diagnostics for detection and predictive modelling of endemicVeterinary parasitology: X · 2019Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
9 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The majority of existing models for predicting disease risk in response to climate change are empirical. These models exploit correlations between historical data, rather than explicitly describing relationships between cause and response variables. Therefore, they are unsuitable for capturing impacts beyond historically observed variability and have limited ability to guide interventions. In this study, we integrate environmental and epidemiological processes into a new mechanistic model, taking the widespread parasitic disease of fasciolosis as an example. The model simulates environmental suitability for disease transmission at a daily time step and 25 m resolution, explicitly linking the parasite life cycle to key weather-water-environment conditions. Using epidemiological data, we show that the model can reproduce observed infection levels in time and space for two case studies in the UK. To overcome data limitations, we propose a calibration approach combining Monte Carlo sampling and expert opinion, which allows constraint of the model in a process-based way, including a quantification of uncertainty. The simulated disease dynamics agree with information from the literature, and comparison with a widely used empirical risk index shows that the new model provides better insight into the time-space patterns of infection, which will be valuable for decision support.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.